VII. Post-modernist organisation theories

Post-modern thought for organisational theory

Post-modernism corresponds to a school of thought at the end of the 20th century, which is widespread in the Anglo-Saxon world and has its sources notably in French philosophers like Jacques DERRIDA, Michel Foucault and Jean-Francois Lyotard.

Nevertheless, it does not translate into any emblematic organisational theory. On the contrary, it refers to a wide variety of approaches and work, which can mainly be defined by a scries of conceptual inputs such as:

Deconstruction

Nothing should be assumed to be acquired or taken for granted. On the contrary, any claim on truth should be systematically deconstructed by tracing it back to its basic hypotheses and by exploring the consequences of the opposite hypotheses.

Example:

The meaning will depend on the object, the action or the person.

Th.Roosevelt told: Talk to 1 person at his or her age, talk to 10 persons as to a 10 years old child, talk to 1000 persons as to a 3-years old baby.

This approach requires developing a certain critical distance. Widespread in management literature, the hypothesis according to which organisations perform better if they have powerful executives and are equipped with good instruments, is hence demoted, replaced by the hypothesis according to which this extra power only strengthens the domination of the employers/capital holders.

The idea is therefore to deconstruct the notion of power, of progress and many others. Deconstruction is the favourite method of post-modernists; it involves shaking up the meanings inside categories and concepts of thought. The post­modernist posture aims to be even more radical than the subjectivist posture (where the idea is that reality does not exist independently of the observer), by claiming that individual subjectivity is in itself constructed. The concepts of the individual and self should be deconstructed just as much as the rest.

Plurality and fragmentation

Post-modernism refutes any claim to unify knowledge. Such an attempt is tantamount to telling each other tall stories. On the contrary, reality is fragmented, split up and contradictory. The idea is to fight against any attempt to simplify and put a fence around the knowledge space. Identities, whether individual or collective, are multifarious, splintered and diffracted. The idea of unity is suspicious. Emphasising the diversity of all things is one of the best ways to question knowledge, truths and falsehoods and what is right, handsome, fair and reasonable. A plurality of voices is also preferable to the distant voice of an intellectual: polyphonic writing where the voice of the researcher is just one among others. It is also about letting the silent, the exploited, those who are on the edge of society and non-humans have their say, as well encouraging what is not said to be said.

The network

Whether in relation to organisations, knowledge or technologies, the notions of network, distribution, decentralisation, flexibility, heterogeneity, eclectics and slack structures feed a wide range of works that do not necessarily have very much to do with each other. Social and economic network data consists of binary social relations, it reflects the presence, absence or strength of relationships among pairs of actors.

E.g., affective network presents, that an actor towards another: likes, loves, hates, admires. Cognitive relations: has no idea, is familiar with, knows.

Mathematically, social networks can be represented as graphs or matrices. In analysis of an organisation, we can present the communication network like this:

Fig. 7. Example of the communication network graph.

The length of the lines does usually mean, because it is representing the close relationship or a distance. The node (vertex, point) means an actor (individual, group, department, organisation...). The line (edge) presents the link, the tie. In the figure above the Research & Development department only consults accountants about financial perspectives through the mediation of Marketing and has not the direct links.

The line between nodes is represented mathematically like this: (a,b). Sometimes, it is useful to add the values. A valued graph has numbers attached to the lines that indicate the strength or frequency or intensity or quantity of the tie between nodes, e.g., the Sales managers have an important volume of formal and informal communications with the accountants:

Fig. 8. Example of the communication network valued graph.

The centrality of a node in a network is a measure of the structural importance of the node. A person's centrality in a social network affects the opportunities and constraints that they face. There are three important aspects of centrality: degree, closeness, and betweenness.

In general, the greater an actor's degree, the more potential influence they have on the network, and vice-versa. For example, in a gossip network, a person who has more connections can spread information more quickly, and will also be more likely to hear more stuff. The greater an actor's degree, the greater the chance that they will catch whatever is flowing through the network, whether good or bad.

Closeness centrality is defined as the total graph-theoretic distance to all other nodes in the network. When a node has a low closeness score (i.e., is highly central), it tends to receive anything flowing through the network very quickly. This is because the speed with which something spreads in a network is a function of the number of links in the paths traversed. Since nodes with low closeness scores are close to all nodes, they receive things quickly. In the case of information about what's happening in the company, this is usually good. In the case of a new disease that is spreading, it is very bad to be one of the first people to get it.

Another measure of an actor's centrality is betweenness. Betweenness centrality is defined as the number of geodesic paths that pass through a node, divided by the number of geodesic paths in total. It is the number of "times" that any node needs go through a given node to reach any other node by the shortest path.. It can be seen as a measure of brokerage.

In a diffusion process, a node that has betweenness can control the flow of information, acting as a gatekeeper.

This is the classic role played by the executive secretary, who can acquire a great deal of unofficial power this way.

We can think of betweenness as a measure of the extent to which a node is in a position to exploit manystructural holes. Astructural hole is a gap in a network: a lack of connection between two nodes. A third party connected to the two unconnected nodes, can sometimes exploit the situation.

Paths & Distances

A path is an alternating sequence of nodes and links, starting and ending with a node. For example, A and B might not be directly connected, but there might be a path that links them: A–B–C–D.

The length of a path is defined as the number of links in it. The length of the path from A to D above is 3, because there 3 links between them. The path from A to B has length 1.

The shortest path between any two points is called a geodesic. The distance between any pair of nodes is defined as the length of a geodesic from one to the other. In other words, the distance is the number of links in the shortest path between the nodes.

If there exists a path of any length that connects a pair of points, they are said to be reachable from each other. A maximal subset of nodes that are mutually reachable is called a component.

A link between nodes that would separate the networks into different components is called a bridge. In other words, a bridge is a link which ties together parts fo the network that otherwise would not be connected at all. A local bridge is a link between nodes A and B which, if removed, would mean that the shortest path linking A and B would be of at least length 3.

The density of a network is defined as the number of ties present divided by the number of ties possible. It is the proportion of possible ties that actually exist.

Cliques are maximal subsets of nodes that are completely connected: all members connected with all others.

Diffusion

The speed with which information travels through a communication network from one node to another is a function of the number of links in the paths linking them. Denser networks have shorter paths, so they transmit information more quickly.

The shape of the network is also important: diffuse networks with little structure diffuse information more quickly than others. Networks broken up into subgroups diffuse information quickly within groups, but have trouble getting information moving between groups.

According to Granovetter, weak ties are particularly important in diffusion. Studies show that most jobs are obtained through network connections, and that among those getting a job via connections, the vast majority get them through weak ties. The reason is that strong ties create dense little knots of people who share and reshare the same old information. Novel information comes in from connections with people outside one's clique. Connections with people outside one's clique (local bridges) are rarely strong ties. Hence, weak ties are especially important for network diffusion.

At the individual level, the number of ties that a person has affects how quickly (and whether) information reaches them. The more ties, the more chances of hearing about something. Closeness provides a good index for how quickly, on average, a person in a network will hear information: it is an estimate of time-until-arrival of information. Closeness is proportional to the average distance from the person to all others via the fastest possible routes. The smaller the number, the more central the person.

Closeness is also important for predicting the speed with which different network structures can solve problems. See the Bavelas & Leavitt experiments in the text.

Date: 2016-03-03; view: 748